Massive amounts of data are collected in almost every corner of the world, and they become the new strategic mechanisms for intelligent businesses. This course covers both foundational knowledge and more advanced practical skills about data processing and analysis. It explores the use of, and techniques used in, exploratory, descriptive, and predictive analytics. Combining technical and statistical skills, analytical thinking, and business acumen, it helps you to harness the power of data analytics.
|Learning materials – Pre-recorded concept videos and associated activity||1hr||Week 1||12 times|
|Tutorial/Workshop 1 – Interactive zoom tutorial||2hrs||Week 1||12 times|
Introduction to data analytics and data science
Data quality issues and pre-processing
Exploratory data analysis and visualisation
Data relationships: association rules and clustering
Machine learning: linear regression, decision trees, deep learning, artificial neural networks
700 Level (Specialised)
|Course Learning Outcomes On successful completion of this course, you should be able to...||Graduate Qualities Completing these tasks successfully will contribute to you becoming...|
|1||Demonstrate a specialised and integrated understanding of contemporary data science and business analytics theories and practices.||
|2||Use data mining, machine learning and data analysis techniques to develop relevant and rigorous models to gain business insights.||
Creative and critical thinker
|3||Investigate, evaluate, and plan the lifecycle of data through an organisation.||Knowledgeable|
|4||Apply computer technology in the solution of business analytics problems.||
Creative and critical thinker
Refer to the USC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.
Standard Grading (GRD)
|High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL).|
Feedback will be provided for the formative exercises in the weekly computer workshops. This feedback will give students immediate feedback on their understanding and progress in the course.
|Delivery mode||Task No.||Assessment Product||Individual or Group||Weighting %||What is the duration / length?||When should I submit?||Where should I submit it?|
|All||1||Examination - not Centrally Scheduled||Individual||30%||
|Week 6||Online Test (Quiz)|
|All||2||Artefact - Technical and Scientific, and Written Piece||Individual||30%||
|Week 11||Online Assignment Submission with plagiarism check|
|All||3||Examination - Centrally Scheduled||Individual||40%||
|Exam Period||Online Test (Quiz)|
|All - Assessment Task 1:Data analytics test|
To learn about the concepts of data analytics using hands on tools. This task enables you to apply computer tools to solve business problems
|Product:||Examination - not Centrally Scheduled|
You will be presented with a data-related challenge, and will use computer tools to manipulate data to solve that challenge. This task will help to build your knowledge of data analysis skills. Further details will be available on Canvas.
|All - Assessment Task 2:Research project|
To undertake a data analytics approach to solve a set of business problems that require the use of appropriately selected data processing and mining approaches.
|Product:||Artefact - Technical and Scientific, and Written Piece|
This is an individual assessment. The assessment will report the set of business problems, data required, and data mining tools selected to solve the selected problems. Further details will be available on Canvas.
|All - Assessment Task 3:Final Examination|
This assessment task will demonstrate your knowledge and application of all material covered in this course.
|Product:||Examination - Centrally Scheduled|
A final examination will be held in the examination period. This two-hour examination will consist of a series of short answer questions to test understanding and application of concepts. This is an individual assessment.
A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Directed study hours may vary by location. Student workload is calculated at 12.5 learning hours per one unit.
Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.
Please note that you need to have regular access to the resource(s) listed below. Resources may be required or recommended.
|Required||Foster Provost,Tom Fawcett||2013||Data Science for Business||n/a||Oreilly & Associates Incorporated|
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Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person. You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.
In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin. This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.
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